Enhancing Product Lifecycle Management through AI-Driven Design Optimization

Artificial intelligence (AI) has gained much popularity in various sectors and has found applications in multiple areas, including occupational health and safety (OHS) risk management of the high-risk construction, mining, and oil and gas sectors. OHS risk management centers on identifying, assessing and controlling occupational risks systematically to prevent work-related injuries, illnesses and deaths. This review presents the advances in AI applications for OHS risk management in these sectors and synthesizes their barriers for better application prospects. In the construction sector, AI can be employed in building information modeling during the design stage to identify and deal with the hazards of building models. AI can be deployed in construction sites through computer vision, sensor networks, knowledge-based systems, and machine learning to capture real-time site conditions, analyze the videos or pictures captured, and provide feedback to workers for appropriate responses. A similar setup involving the same components is also used for managing the OHS risks of surface or underground mining, particularly for monitoring the environmental conditions, detecting the presence of hazardous gases, and identifying hazards in locations that are remote and difficult to assess. Sensors can be attached to personal protective equipment and watches and the signals transmitted via Bluetooth to permit data collection for analysis and response by AI. In the oil and gas sector, sensors are extensively used to collect process safety data from wells, pipelines, valves, etc. for analytical and predictive Al. Al, especially, machine learning is used to create personalized training for workers based on their learning pace and characteristics. However, the major barriers identified are high cost, lack of support and skilled employees, ethical issues, and the uncertainty of AI.

The oil and gas sector is a critical driver of Nigeria’s economy, contributing significantly to national revenue and employment. However, the industry faces considerable challenges related to legal compliance and operational efficiency, which hinder its potential. This paper proposes a comprehensive Legal Compliance and Efficiency Framework tailored for Nigeria's oil and gas sector. The framework integrates regulatory requirements, best practices, and innovative approaches to enhance compliance and operational performance. The first component of the framework emphasizes the importance of understanding and adhering to both local and international regulations. This involves a thorough review of existing laws governing the sector, including environmental regulations, safety standards, and tax obligations. Stakeholder engagement is crucial, as it fosters collaboration between government agencies, industry players, and communities, ensuring a holistic approach to compliance. The second component focuses on operational efficiency, advocating for the adoption of advanced technologies such as automation, data analytics, and artificial intelligence. These technologies can optimize processes, reduce costs, and enhance decision-making. By leveraging data-driven insights, companies can identify inefficiencies and implement corrective measures promptly. Moreover, the framework highlights the need for capacity building and training programs for personnel within the sector. Equipping employees with the necessary skills and knowledge ensures that they can navigate complex regulatory landscapes and contribute to enhanced operational practices. The final component addresses the significance of monitoring and evaluation mechanisms to assess compliance and efficiency continuously. This includes establishing key performance indicators (KPIs). and regular audits to identify areas for improvement and ensure adherence to regulations. In conclusion, the proposed Legal Compliance and Efficiency Framework aims to create a sustainable and competitive oil and gas sector in Nigeria. By aligning legal obligations with efficient operational practices, the framework seeks to foster economic growth while safeguarding the environment and promoting social responsibility. Keywords: Legal Compliance, Operational Efficiency, Oil and Gas Sector, Nigeria, Regulatory Framework, Technology Adoption, Capacity Building.

The oil and gas sector, a cornerstone of the global energy supply, is poised at the brink of a technological renaissance as it embraces Artificial Intelligence (AI). This paradigm shift is driven by the industry's imperative to enhance efficiency, bolster safety, and notably, optimize costs amidst volatile market conditions and escalating operational complexities. This paper presents an in-depth exploration of the multifaceted applications of AI technologies in streamlining cost management across various phases of oil and gas projects—from exploration to distribution. At the heart of this exploration is the elucidation of AI's transformative role in predictive analytics, automation, and decision support systems. By harnessing the power of machine learning algorithms, the industry can predict equipment failures, optimize maintenance schedules, and ensure uninterrupted production, thereby significantly reducing downtime costs. Furthermore, AI-driven data analytics enable the identification of patterns and insights from vast datasets, leading to more informed and cost-effective decision-making. The paper also delves into the integration of AI in operational domains such as drilling optimization, where AI algorithms analyze geological data to determine optimal drilling locations and parameters, thus minimizing the risk of costly non-productive time. Similarly, in the realm of supply chain management, AI facilitates dynamic routing and inventory control, curtailing logistical expenses. Highlighting case studies and empirical data, the paper underscores the tangible benefits realized by early adopters of AI in the industry, showcasing a potential reduction in operational costs and an increase in efficiency that often surpasses traditional methodologies. This detailed examination not only charts a course for future AI-driven endeavors in the oil and gas sector but also serves as a clarion call for stakeholders to navigate the intricacies of digital transformation strategically. In conclusion, the paper posits that the integration of AI stands as a beacon of innovation, promising not only cost optimization but also a sustainable and resilient future for the oil and gas industry. The findings herein aim to galvanize industry leaders, policymakers, and technologists to foster a collaborative ecosystem where AI can flourish, driving the industry towards a new horizon of operational excellence.

Recently, artificial intelligence (AI) technology receives extensive attention in the manufacturing field. As the core technology, it generates considerable interest among smart manufacturing and Industry 4.0 strategy. Product lifecycle management (PLM) copes with various kinds of engineering, business, and management activities concerning a product throughout its whole lifecycle—from the inception of an intangible concept through the recycling of a finished product. In the context of smart manufacturing, this paper reviews various theories, algorithms, and technologies of AI to different stages of PLM (i.e., product design, manufacturing, and service). A structured roadmap is presented to navigate the future research and application of AI in PLM. This paper also discusses the opportunities and challenges of applying AI for PLM.

What explains the choice of corporate political strategy in environmental politics? Drawing on recent models of actor strategy formation in political economy, this article argues that basic material interests of firms are translated into strategies in the context of institutional environments. I advance a typological model that posits how distributional effects—positive versus negative—and perceived regulatory pressure—low versus high—interact in leading firms to adopt one of four ideal-type strategies: opposition, hedging, support, and non-participation. This article examines the model through the case of corporate strategies in the making of the European Union’s Emission Trading Scheme. The article contributes to theory-building on business strategy in environmental politics by offering a probabilistic explanatory model, and it flags hedging strategies as an increasingly prevalent form of business behavior.

The fourth industrial revolution is actively developing in the world. Since 2011 it has become widespread. Affecting all areas of life activity, economy, which manifests itself in the qualitative reorganization of its mechanisms, creates new adapted management models, changes the nature of production processes. The digital revolution opens up new opportunities for using digital networks, agile production systems, and new business models for all stages of production. The faster the company makes this transition, the better its chances of succeeding and outperforming its competitors. Product lifecycle management (PLM) refers to the management of data and processes used in the design, engineering, manufacturing, sales, and service of a product across its entire lifecycle. The introduction of digital technologies occurs at all stages of the product lifecycle: product development, technology development, preparation and launch of production, product production, its operation and maintenance. PLM affects the ideology of creating a new product: to reduce the time to market and save resources, the system pushes to shift the labor intensity from the “production” stage to the “design” stage. The purpose of this study is to analyze product lifecycle management in agile management and to relate it to the current state of the Russian industrial development. Theoretical and practical aspects of this concept are given. The example of PLM implementation in the Russian industrial sector is presented. The Kalashnikov concern has built a product lifecycle management system. Today, the development of production of an industrial enterprise is impossible without a clear understanding of all stages of life cycle management of the product that it produces. Summarizing, PLM is offered as a promising concept for Russian economy.

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

This study examines the cognitive factors that determine corporate environmental accounting disclosures (CEAD). The population consists of all the fourteen (14) listed oil and gas firms in Nigeria. Panel data were obtained from the annual reports and accounts of the firms for the period of 2010 to 2019. A correlational research design was used and the data were analyzed using the Generalized Least Square regression (random model). The study found that firm size; leverage and multi-national companies have positive significant influence on the CEAD of listed oil and gas firms in Nigeria. Whilst firm growth has a negative significant relationship with the CEAD of listed oil and gas firms in Nigeria. It is concluded that larger firms and multi-national companies in the Nigerian oil and gas sector have high likelihood of disclosing environmental accounting information. Thus, it is recommended that the management of listed oil and gas firms in Nigeria should expand their size by acquiring more assets, maintain a consistent growth by exploring more opportunities while improving their gearing ratio to ensure a stable balance between the proportion of debt and assets. It therefore, highlighted the need for Securities and Exchange Commission (SEC) to come up with enabling laws geared towards ensuring that listed oil and gas firms in Nigeria embrace CEAD. Furthermore, Global Environmental Disclosure Index (GEI) should be considered as the most acceptable yardstick for measuring environmental accounting by the listed oil and gas firms in Nigeria.

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

This research study aims to examine the impact of sustainability on firm performance and analyze how vertical integration moderates the connection between performance and sustainability in the oil and gas sector. We analyzed a sample dataset of oil and gas companies from the top ten oil-producing countries spanned over ten years (2011–2020). The pool-fixed regression technique confirms that sustainability and its three components, i.e., social, environmental, and governance, are negatively related to performance. However, vertical integration moderated the connection between sustainability and performance in the case of the oil and gas sector. We have identified firm size, age, and return share price positively related to firm performance in the oil and gas industry. At the same time, the debt ratio negatively impacts the firm’s performance. The findings are significant for the management of oil and gas firms and the policymakers and regulatory authorities of oil-producing and exporting countries.

The paper analyzes, first, the contribution of hydrocarbon sector to GDP, export proceeds, and budget revenues, and, second, the impact of volatility of international oil prices on the Russian economy. It is found that the importance of the oil and gas sector for the Russian economy adjusted for changes in oil prices is steadily declining since 2000. The potential impact of hydrocarbon prices volatility on the Russian monetary situation is weakening. It is demonstrated that 6% to 11% of GDP is shifted from the oil and gas sector to the trade sector via transfer pricing mechanism. Taking this fact into account alters crucially estimated tax burden for the hydrocarbon sector: it is found to be below that for the rest of the industry until 2001, and close to the latter subsequently. The contribution of oil and gas taxes to the budget does not differ much from the share of this sector in the GDP. Recent increase in the share of windfall oil revenues taken to the budget has not been supported with adequate fiscal policies. Current fiscal situation remains robust, but continuation of these trends may make the budget vulnerable to external volatility. The oil sector has played a critical role in the recent economic growth. Still the effect of favorable terms of trade on growth has not been significant, as its possible impact has been damped down with measures taken by the monetary authorities to smooth the effect of external volatility.

The COVID-19 pandemic came with disruptions in every aspect of human existence, with all the sectors of the economies of the world affected greatly. In the health sector, the pandemic halted and reversed progress in health and subsequently shortened life expectancy, especially in developing and underdeveloped nations. On the other hand, machine learning and artificial intelligence contributed a great deal to the handling of the pandemic globally. Therefore, the current study aimed to assess the role played by artificial intelligence and machine learning in addressing the dangers posed by the COVID-19 pandemic, as well as extrapolate the lessons on the fourth industrial revolution and sustainable development goals. Using qualitative content analysis, the results indicated that artificial intelligence and machine learning played an important role in the response to the challenges posed by the COVID-19 pandemic. Artificial intelligence, machine learning, and various digital communication tools through telehealth performed meaningful roles in scaling customer communications, provided a platform for understanding how COVID-19 spreads, and sped up research and treatment of COVID-19, among other notable achievements. The lessons we draw from this is that, despite the disruptions and the rise in the number of unintended consequences of technology in the fourth industrial revolution, the role played by artificial intelligence and machine learning motivates us to conclude that governments must build trust in these technologies, to address health problems going forward, to ensure that the sustainable development goals related to good health and wellbeing are achieved.

Artificial intelligence (AI) is transforming global industries, including the oil and gas sector. As part of ongoing digital transformation, AI has become a key driver of well digitalisation, improving how wells are designed, drilled, monitored, and managed. Through advanced data analytics, predictive modelling, and real-time automation, AI enhances operational decision-making and optimises complex processes. Well digitalisation integrates digital technologies, automation, and data-driven intelligence across the entire well life cycle, enabling systems to process large datasets, identify patterns, predict outcomes, and recommend actions with minimal human intervention. This results in higher efficiency, reduced non-productive time, improved safety, and quicker responses to operational challenges. However, achieving effective well digitalisation with AI remains difficult due to cultural and organisational constraints, including resistance to change, limited digital literacy, siloed structures, traditional work norms, and reliance on legacy systems. This paper investigates these barriers and outlines strategies to support sustainable transformation. Using a hybrid approach—industry field observations complemented by a systematic literature review—it finds that success requires a strong digital culture, strategic investment in digital skills, organisational alignment, and visionary leadership. Addressing these factors enables organisations to unlock AI’s full potential, strengthen resilience, and remain competitive in the evolving energy landscape.

The article has an overview of modern digital technologies that are introduced to optimize the operation of the railway complex: cloud storage, big data, Internet of things, artificial intelligence, blockchain. The authors pay attention to the advantages and peculiarities of digital railways, give practical examples of successful projects on Russian and foreign railways. Potential problems related to the introduction of new digital systems into existing IT infrastructure, adaptation of personnel and cybersecurity are highlighted.

This study evaluates AI’s effectiveness in boosting real-time decision-making and supply chain agility in West African ports. Utilizing Structural Equation Modeling (SEM), data from 250 supply chain experts across several countries, including Ghana and Nigeria, were analyzed. Results indicate significant enhancements in supply chain agility, particularly through improved data processing speed, system integration, prediction accuracy, and user interface quality, with the latter having the most substantial impact. The study underscores the importance of user-friendly AI systems, supported by Dynamic Capabilities Theory, which facilitates organizational adaptability to market changes. Recommendations focus on developing AI systems with robust user interfaces and ensuring seamless integration with existing IT infrastructures. This research contributes to the literature by empirically demonstrating AI’s role in improving operational adaptability and filling theoretical gaps, with a unique regional focus and methodological approach.